For people who keep asking what to build in AI Engineering.
➣ Build your own Agent Orchestrator (deterministic state machine, no LangChain)
➣ Build your own Context Assembler (token-budgeted memory + retrieval + tools)
➣ Build your own MCP Server and Client (raw JSON-RPC, no SDK)
➣ Build your own Retrieval Stack (chunker + BM25 + dense + reranker from scratch)
➣ Build your own Eval Harness (trajectory grading + CI regression gates)
➣ Build your own Model Router (cost/latency/quality routing with fallbacks)
➣ Build your own Semantic Cache (embedding similarity + hit-rate tracking)
➣ Build your own Guardrails Middleware (injection detection + PII redaction)
➣ Build your own Streaming Proxy (SSE with TTFT and ITL metrics)
➣ Build your own Durable Workflow Engine (checkpoint/resume, mini-Temporal)
➣ Build your own LLM Tracer (OpenTelemetry-style spans for every hop)
➣ Build your own Sandboxed Tool Executor (isolated execution, resource limits)
➣ Build your own Prompt Registry (versioning + A/B routing + rollback)
➣ Build your own Data Flywheel (feedback → synthetic data → LoRA loop)
➣ Build your own Multi-Agent Consensus (weighted voting + judge + escalation)
Pick 3. Build them from scratch. Document every decision.
Most people import libraries.
Builders understand them.
(Bookmark & Repost.)
To become cracked at Inference engineering by end of this year, just download any open weights model & build an inference engine around it.
You'll learn alot about CUDA, GPU kernels, resource allocations, parallelism, LLM internals & a lot of maths (etc).
Hi! I’m an FDE at OpenAI, and we’re thinking about publishing some of our work and learnings in a series of technical blog posts. What would you like us to write about?
Doing the bare minimum is worse than not doing something. Everything has an opportunity cost, the time you spend doing something you don't feel like doing is a waste of your own and everyone else's time.
When you sign up for something, put genuine efforts in it. When you stop feeling like putting efforts, it is a sign to find something else that you can put your heart and soul into.
He walked into a restaurant during a quiet Tuesday afternoon.
He didn't pitch software. He showed the owner his phone.
"Type an order like a customer would."
The chatbot took the order, confirmed the table, logged it in a Google Sheet, and sent it to the kitchen in under two seconds.
The owner asked how much.
"$500/month. First month free. If it doesn't save your staff real time, you owe nothing."
That conversation has played out 30 times. It closed 30 times.
30 clients. $500/month each. 4 hours of setup per restaurant. 93% margin.
Over 1 million restaurants in the US. Fewer than 3% have this.
He just keeps walking in during quiet Tuesday afternoons.
This AI System Design guide teaches RAG better than most courses.
And I'm giving it away for free (Only for First 4500)
Inside:
• RAG fundamentals & chunking strategies
• Hybrid retrieval (BM25 + vector search)
• Production-level RAG architecture
• Evaluation & RAGAS metrics
• Hallucination reduction techniques
• End-to-end LLM system design
How to get it:
• Follow me (must so I can DM)
• RT + Like
• Comment "book"
I'll dm you
This AI System Design guide teaches RAG better than most courses.
And I'm giving it away for free (Only for First 4500)
Inside:
• RAG fundamentals & chunking strategies
• Hybrid retrieval (BM25 + vector search)
• Production-level RAG architecture
• Evaluation & RAGAS metrics
• Hallucination reduction techniques
• End-to-end LLM system design
How to get it:
• Follow me (must so I can DM)
• RT + Like
• Comment "book"
I'll dm you
Nearly every ambitious person I know who has dived into AI is working harder than ever, and longer hours than ever.
Fascinating dynamic tbh.
I have NEVER worked this hard, nor had this much fun with work.
Born...
-too late to get a job at Google by inverting binary tree
-too early to be in school & master everything using LLMs
-exactly at the time when AI started replacing humans